On Robust Training of Regression Neural Networks

0525292 - ÚI 2021 RIV CH eng C - Konferenční příspěvek (zahraniční konf.)
Kalina, Jan - Vidnerová, Petra
On Robust Training of Regression Neural Networks.
Functional and High-Dimensional Statistics and Related Fields. Cham: Springer, 2020 - (Aneiros, G.; Horová, I.; Hušková, M.; Vieu, P.), s. 145-152. Contributions to Statistics. ISBN 978-3-030-47755-4. ISSN 1431-1968.
[IWFOS 2020/2021: International Workshop on Functional and Operatorial Statistics /5./. Online (CZ), 23.06.2021-25.06.2021]
Grant CEP: GA ČR(CZ) GA19-05704S; GA ČR(CZ) GA18-23827S
Institucionální podpora: RVO:67985807
Klíčová slova: Neural networks * Nonlinear regression * Nonlinear quantiles * Robustness * Optimization
Obor OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Estimation, prediction or smoothing of curves represents a fundamental task of functional data analysis. Nonlinear regression methods allow to search for the best-fit curves explaining the dependence of a response variable on available independent variables. Neural networks, commonly used for the task of nonlinear regression, are however highly vulnerable to the presence of outlying measurements in the data. New robust versions of common types of neural networks, namely multilayer perceptrons and radial basis function networks, are proposed here based on nonlinear regression quantiles or highly robust loss functions. Three datasets are analyzed to illustrate the performance of the novel robust approaches, which turn out to outperform standard neural networks or other competing regression tools over contaminated data.
Trvalý link: http://hdl.handle.net/11104/0309464